This thesis investigates different aspects of spectrum sensing in cognitive radio (CR) technology. First a probabilistic inference approach is presented which models the decision fusion in cooperative sensing as a probabilistic inference problem on a factor graph. This approach allows for modeling and accommodating the uncertainties and correlations in the cooperative sensing system. A constraint in the cognitive radios is the lack of knowledge about the primary signal and channel gain statistics at the secondary users. Therefore, a practical composite hypothesis approach is proposed which does not require any prior knowledge or estimates of these unknown parameters. Detection delay is an important performance measure in spectrum sensing. Quickest detection aiming to minimize detection delay has been studied in other contexts, and we apply it here to spectrum sensing. To combat the destructive channel conditions such as fading, various cooperative schemes based on the cumulative sum (CUSUM) algorithm are considered in this thesis. Furthermore, cooperative quickest sensing with imperfectly known parameters is investigated and a new solution is derived, which does not require any parameter estimation or iterative algorithm. In cognitive radios, there is a fundamental trade-off between the achievable throughput by the CRs and the level of protection for the primary user. In this thesis, this trade-off is formulated for the quickest sensing-based CRs. By throughput analysis, it is shown that for the same protection level to the primary user, the quickest sensing approach results in significantly higher average throughput compared to that of the conventional block sensing approach.

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Abstract An improvement and more accuracy method for spectrum analysis has been achieved in this thesis. There are three major parts in this thesis: the signal parameter estimation, the optimization of spectrum analysis, and the supervision to electric machinery. All these parts suggest the improvement ways to theories and applications of signal process. Parameter estimation is the base of dynamic designs, controls, and supervisions. This thesis infers the complete method to estimate parameters. The method estimates signal parameters in frequency domain. In electric machinery analysis, the most signals can consist of complex exponents. The component parameters include frequency, damping, amplitude, and phase. Basing on the damping existed or not, signals can be classified into two parts: periodic and non-periodic. Each complex exponent component will produce its band on spectrum. This method references the scales with highest amplitudes to estimate exact parameters. In suitable conditions, these mathematical equations can be simplified substantially to save computing time. The developed technologies of spectrum analysis take FFT to deal with the time-frequency transform work extensively. However, the sample of discrete signal is at random, and FFT suffers specific restrictions. When FFT transforms signal into frequency domain, the signal will cause errors on spectrum inevitably. This thesis corrects the errors by the optimization method. When frequency scales can match with signal characteristics, the picket-fence effect and leakage effect that the signal caused on spectrum will decrease to minimum. This method consists of three new technologies: parameter estimation, selection for optimal scale parameters, and adjustable spectrum. The method not only displays signal parameters on spectrum exactly and clearly, but also keeps the ability of fast process. When analyzing the more complex signal, the result of optimization will be restricted. Under this condition, the method can focus on the partial components and analyze them, then the result will keep accurate. This thesis combines supervisory technologies via a signal measurement. The signal sampling of these technologies is more convenient and simple. The system monitors operating conditions and fault conditions of the electric machinery with sound signal analysis. This signal analysis not only keeps normal measurement in the place which other signals can¡¦t be detected, but also can expand the monitoring ability. In operation conditions, the system monitors the speed and the input power of electric machinery through sound signal analysis. In fault conditions, the system recognizes type of fault under variation loads successfully. The recognition system is established by artificial neural network. The improvement of recognition ability is also discussed in this thesis. The methods discussed in the thesis give powerful estimation method for the signal analysis accurately and practically.

In this thesis, the problem of spectrum sensing in cognitive radio communication networks is considered. This thesis has developed a robust decision fusion scheme that can perform well when the interference caused by other PUs is present. Specifically, the proposed detection scheme is based on fusing the local decisions from energy detectors (EDs) and cyclostationarity- based detectors (CDs). Our proposed fusion scheme is different from other power spectrum sensing technology being developed so far in that other fusion technology are based on fusing the local decisions from the same type of detectors. Our proposed fusion scheme can take the advantage of both EDs and CDs. We compare the proposed scheme with the schemes fusing the same type of detectors, and the results confirm that the proposed scheme is more robust against the possible interference.

Thesis--Cambridge. / Typescript (carbon) with ms. corrections. Includes bibliographical references.

Thesis (M. Sc.)--Cambridge. / Typescript.

Thesis (M. Sc.)--York University, 1997. Graduate Programme in Earth and Space Science. / Typescript. Includes bibliographical references (leaves 83-85). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://wwwlib.umi.com/cr/yorku/fullcit?pMQ27334.

Thesis (Ph. D.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 166-190).

Minor physical anomalies (MPA) are mild congenital malformations in external physical abnormalities that are observed in neurodevelopmental disorders such as velocardiofacial syndrome (more commonly known as 22q11.2 deletion syndrome), autism and schizophrenia. It has been reported that about three-quarters of MPAs can be found in the craniofacial region, such as low-seated ears, high-steepled palate, altered interorbital distance. Furthermore, both eye and brain development are tightly linked, and interorbital distance and optic chiasmic angle in adulthood are perinatally fixed. Therefore my study investigated MPAs of the optic system in two neurodevelopmental disorders, 22q11.2 deletion syndrome and autism spectrum disorders (ASD). The four selected MPAs included 1) inter-orbital distance, 2) the optic chiasmic angle, 3) anterior extension of optic nerves ‘a’, and 4) posterior extension of optic nerves ‘b’. They were investigated in two individual studies, the first comprising 17 adults with ASD and 21 controls while the second comprised 27 adults with 22q11.2 deletion syndrome and 28 controls. Their MRI scans were analysed with respect to the MPAs. The main finding was that adults with ASD had significantly larger total brain volume which was positively correlated with the anterior extension of optic nerves ‘a’, but not the other optical MPAs. This suggested that the larger brain volume was more related to frontal brain enlargement. As no relevant significant results were obtained in the controls nor adults with 22q11.2 deletion syndrome, such finding appears pertinent to ASD adults, rather than that to a genetic neurodevelopmental disorder. However, this finding is only preliminary, and other neurodevelopmental disorders should be included for comparison with ASD and healthy controls. The use of MRI appears to be a feasible tool to assess MPAs. Future work will focus on whether these observations can be replicated across the life-span by evaluating these parameters in children with ASD, as well as adults with other neurodevelopmental disorders. / published_or_final_version / Psychiatry / Master / Master of Philosophy

ITC/USA 2012 Conference Proceedings / The Forty-Eighth Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2012 / Town and Country Resort & Convention Center, San Diego, California / Growing pressures on today's testing resources are driving the need for a change in the way telemetry is currently being done. As systems advance and become more complex, testing these systems becomes more difficult due to budget, schedule and test resource constraints. These pressures create the need to support more concurrent testing with ever increasing numbers of participants and bandwidth requirements, all while available resources are diminishing. In order to continue to provide support to the war fighter through timely and efficient testing of new systems, the test infrastructure needs to be updated to become more agile and efficient. We will examine the application of innovative new technologies and concepts to increase the capabilities of the testing infrastructure in the presence of shrinking resources. By leveraging advances in wireless technologies, telemetry networks and other technologies, we will present alternatives to the current telemetry paradigm.